Poor antibiotic prescribing practices can increase bacterial resistance in the community and harm patients if an inappropriate antibiotic is chosen. The goal of this work is to automate and validate a tool intended to help providers better choose antibiotics before culture results are available. The tool uses microbiology data from the hospital and clinical information from the patient to predict which antibiotic regimen would be most likely to cover the patient's infection. The study will be done at two sites in order to improve the generalizability of the findings, and to create a tool that can be easily disseminated to other sites. The work will be accomplished in three aims. In the first aim, the microbiology data from two institutions will be formatted so that it can be used in predictive models. Experts including microbiologists, physicians, and pharmacists at both institutions will create rules that will be used to create an automated algorithm for formatting the data. In the second aim, predictive models will be developed and validated on a retrospective cohort at both institutions. Next, prescribing rules developed by subject matter experts will be added to the model output. This will allow the tool to output a recommended antibiotic regimen based on the rules and predictive modeling. The investigators will assess the quality of this recommendation by comparing it to the actual initial antibiotic given to each patient. The percentage of infections that were covered by each antibiotic regimen will be compared, as will the breadth of coverage and cost of each regimen. In the third aim, a user-friendly interface will be created within the electronic health record to display the outputs of the tool created in aim two. This work is the first phase of a larger project intended to study whether giving providers access to up-to-date, local, personalized microbiology data will improve prescribing practices and ultimately patient care and outcomes.